ChatGPT for Automated Cross-Checking of Authors' Conflicts of Interest Against Industry Payments

Conrad Safranek; Christina Liu; Rhys Richmond; Trinithas Boyi; Ryan Rimmer; R Peter Manes

doi:10.1002/ohn.720

ChatGPT for Automated Cross-Checking of Authors' Conflicts of Interest Against Industry Payments

Otolaryngol Head Neck Surg. 2024 Mar 15. doi: 10.1002/ohn.720. Online ahead of print.

Authors

Conrad Safranek^{1

2}, Christina Liu¹, Rhys Richmond¹, Trinithas Boyi¹, Ryan Rimmer¹, R Peter Manes¹

Affiliations

¹ Department of Surgery, Division of Otolaryngology, Yale School of Medicine, New Haven, Connecticut, USA.
² Section for Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, Connecticut, USA.

PMID: 38488302
DOI: 10.1002/ohn.720

Abstract

Objective: The Centers for Medicare & Medicaid Services "OpenPayments" database tracks industry payments to US physicians to improve research conflicts of interest (COIs) transparency, but manual cross-checking of articles' authors against this database is labor-intensive. This study aims to assess the potential of large language models (LLMs) like ChatGPT to automate COI data analysis in medical publications.

Study design: An observational study analyzing the accuracy of ChatGPT in automating the cross-checking of COI disclosures in medical research articles against the OpenPayments database.

Setting: Publications regarding Food and Drug Administration-approved biologics for chronic rhinosinusitis with nasal polyposis: omalizumab, mepolizumab, and dupilumab.

Methods: First, ChatGPT evaluated author affiliations from PubMed to identify those based in the United States. Second, for author names matching 1 or multiple payment recipients in OpenPayments, ChatGPT undertook a comparative analysis between author affiliation and OpenPayments recipient metadata. Third, ChatGPT scrutinized full article COI statements, producing an intricate matrix of disclosures for each author against each relevant company (Sanofi, Regeneron, Genentech, Novartis, and GlaxoSmithKline). A random subset of responses was manually checked for accuracy.

Results: In total, 78 relevant articles and 294 unique US authors were included, leading to 980 LLM queries. Manual verification showed accuracies of 100% (200/200; 95% confidence interval [CI]: 98.1%-100%) for country analysis, 97.4% (113/116; 95% CI: 92.7%-99.1%) for matching author affiliations with OpenPayments metadata, and 99.2% (1091/1100; 95% CI: 98.5%-99.6%) for COI statement data extraction.

Conclusion: LLMs have robust potential to automate author-company-specific COI cross-checking against the OpenPayments database. Our findings pave the way for streamlined, efficient, and accurate COI assessment that could be widely employed across medical research.

Keywords: CMS openpayments; ChatGPT; artificial intelligence; biologics; conflict of interest; data analysis automation; large language models; nasal polyps; pharmaceutical funding.